Search Results for author: Gurtej Kanwar

Found 11 papers, 1 papers with code

Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions

no code implementations18 Jul 2022 Ryan Abbott, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Betsy Tian, Julian M. Urban

This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as stochastic estimators for the fermionic determinant.

Flow-based sampling in the lattice Schwinger model at criticality

no code implementations23 Feb 2022 Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

In this work, we provide a numerical demonstration of robust flow-based sampling in the Schwinger model at the critical value of the fermion mass.

Flow-based sampling for multimodal distributions in lattice field theory

no code implementations1 Jul 2021 Daniel C. Hackett, Chung-Chun Hsieh, Michael S. Albergo, Denis Boyda, Jiunn-Wei Chen, Kai-Feng Chen, Kyle Cranmer, Gurtej Kanwar, Phiala E. Shanahan

Recent results have demonstrated that samplers constructed with flow-based generative models are a promising new approach for configuration generation in lattice field theory.

Flow-based sampling for fermionic lattice field theories

no code implementations10 Jun 2021 Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan

Algorithms based on normalizing flows are emerging as promising machine learning approaches to sampling complicated probability distributions in a way that can be made asymptotically exact.

Machine Learning and Variational Algorithms for Lattice Field Theory

no code implementations3 Jun 2021 Gurtej Kanwar

Finally, we demonstrate that flow-based MCMC can mitigate critical slowing down and observifolds can exponentially reduce variance in proof-of-principle applications to scalar $\phi^4$ theory and $\mathrm{U}(1)$ and $\mathrm{SU}(N)$ lattice gauge theories.

BIG-bench Machine Learning

Real-time lattice gauge theory actions: unitarity, convergence, and path integral contour deformations

no code implementations3 Mar 2021 Gurtej Kanwar, Michael L. Wagman

The character expansion defining the HFK action is divergent, and in this work we apply a path integral contour deformation to obtain a convergent representation for U(1) HFK path integrals suitable for numerical Monte Carlo calculations.

High Energy Physics - Lattice High Energy Physics - Phenomenology High Energy Physics - Theory Nuclear Theory Quantum Physics

Path integral contour deformations for observables in $SU(N)$ gauge theory

no code implementations29 Jan 2021 William Detmold, Gurtej Kanwar, Henry Lamm, Michael L. Wagman, Neill C. Warrington

We define a family of contour deformations applicable to $SU(N)$ lattice gauge theory that can reduce sign and signal-to-noise problems associated with complex actions and complex observables.

High Energy Physics - Lattice Statistical Mechanics Nuclear Theory

Introduction to Normalizing Flows for Lattice Field Theory

no code implementations20 Jan 2021 Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan

This notebook tutorial demonstrates a method for sampling Boltzmann distributions of lattice field theories using a class of machine learning models known as normalizing flows.

BIG-bench Machine Learning

Sampling using $SU(N)$ gauge equivariant flows

no code implementations12 Aug 2020 Denis Boyda, Gurtej Kanwar, Sébastien Racanière, Danilo Jimenez Rezende, Michael S. Albergo, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan

We develop a flow-based sampling algorithm for $SU(N)$ lattice gauge theories that is gauge-invariant by construction.

Equivariant flow-based sampling for lattice gauge theory

no code implementations13 Mar 2020 Gurtej Kanwar, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan

We define a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge-invariant by construction.

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